The present invention generally relates to a pattern recognition technique, namely to a pattern recognition system capable of rejecting an object difficult to be pattern-recognized without performing a pattern recognition in the case that this object difficult to be pattern-recognized appears, and the reliability of pattern-recognizing results is low. More specifically, the present invention is directed to a pattern recognition system suitable for a urinary sediment analyzer capable of classifying particles contained in urine.
A urinary sediment examination is such an examination that solid components such as blood cells and epithelial cells contained in urine are investigated, and then sorts and amounts of the respective solid components are reported. Conventionally, this urinary sediment examination has been carried out in the following manner. That is, a predetermined amount of urine is centrifuged to acquire sediment components, these sediment components are stained, and then the stained sediment components are collected as a sample on a smear preparation. Thereafter, a technician observes this sample by using a microscope. The respective components are classified based upon features such as a shape and stainability. Since even same components show various shapes, there is much possibility that the classification of these components becomes difficult. Also, since a urine sample is continuously exposed to the open air after it has been acquired, there are some cases that contamination existing in air enters into the urine sample. As to such subjects, the technician does not classify these contaminants, but may classify and count only typical subjects which can be correctly classified.
The techniques capable of automatically executing the urinary sediment examination are disclosed in, for instance, JP-A-57-500995 (WO81/03224), JP-A-63-94156, and JP-A-5-296915, in which solid components, or particles contained in the urine are photographed as still images. In these conventional techniques, the sample is supplied to pass through the flow cell having the specific shapes, and the particles contained in the sample are supplied to flow into the wide photographing region. When the solid components are detected within the sample, the flash lamp is turned ON, so that the enlarged images of the solid components contained in the urine are photographed as the still images. To automatically analyze the sediment components photographed as the still images, first of all, after the region of the sediment components is segmented from the background region thereof on the image, the image feature parameters in the region of the sediment components are calculated. The classification is carried out based on these feature parameters. As the image feature parameters, an area, a perimeter, and a mean color density are employed. On the other hand, as the technique for segmenting the region of the solid component from the background region on the image, there is described in, for example, JP-A-1-119765 entitled "REGION SEGMENTING METHOD OF BLOOD CELL IMAGE". In this technique, the image region is segmented in the color space by employing the threshold calculated from the gray level histogram.
As the technique for classifying a subject from an image feature parameter, for example, JP-A-58-29872 and JP-A-3-131756 describe the classification of the blood cell image. JP-A-58-29872 describes that either the discrimination theory which is combined by the statistical discrimination function in the multiple stage based on the image feature parameters or the decision tree theory is employed. JP-A-3-131756 describes that the multi-layer network is employed as the recognition theory. When the pattern recognition is carried out by utilizing the network structure, the following methods are normally used. First, the output nodes whose quantity is equal to that of the classes in which subjects are to be classified are prepared, and then these output nodes are allocated to these classes one by one. Next, the network is constructed by employing the training pattern in such a manner that when a certain pattern is entered, the output of the output node corresponding to the class belonging to the input pattern, among the outputs from the respective output nodes, becomes maximum. When an unknown pattern is actually recognized, the unknown pattern is inputted. Assuming now that the class corresponding to the output node for outputting the maximum value among the output values of the respective output nodes is recognized as the class belonging to the unknown pattern, this class is displayed as the recognized result. JP-A-3-131756 further describes that the threshold is provided to the output value, and when the maximum output value is smaller than, or equal to this threshold, the sample cannot be classified. Also, JP-A-4-1870 describes that the confirmation degree is compared with the threshold; when the confirmation degree is greater than the threshold, the output result is used as the recognized result, whereas when the confirmation degree is smaller than the threshold, the output result is rejected. As a consequence, the reliability of the recognized result can be increased JP-A-4-294444 describes that the output reliability of the neural network is evaluated by the reliability evaluating means.